Using interval weights in MADM problems

The choice of weights vectors in multiple attribute decision making (MADM) problems has generated an important literature, and a large number of methods have been proposed for this task. In some situations the decision maker (DM) may not be willing or able to provide exact values of the weights, but this difficulty can be avoided by allowing the DM to give some variability in the weights. In this paper we propose a model where the weights are not fixed, but can take any value from certain intervals, so the score of each alternative is the maximum value that the weighted mean can reach when the weights belong to those intervals. We provide a closed-form expression for the scores achieved by the alternatives so that they can be ranked them without solving the proposed model, and apply this new method to an MADM problem taken from the literature.Este trabajo forma parte del proyecto de investigación: MEC-FEDER Grant ECO2016-77900-P

Regions of rationality: Maps for bounded agents

An important problem in descriptive and prescriptive research in decision making is to identify “regions of rationality,” i.e., the areas for which heuristics are and are not effective. To map the contours of such regions, we derive probabilities that heuristics identify the best of m alternatives (m > 2) characterized by k attributes or cues (k > 1). The heuristics include a single variable (lexicographic), variations of elimination-by-aspects, equal weighting, hybrids of the preceding, and models exploiting dominance. We use twenty simulated and four empirical datasets for illustration. We further provide an overview by regressing heuristic performance on factors characterizing environments. Overall, “sensible” heuristics generally yield similar choices in many environments. However, selection of the appropriate heuristic can be important in some regions (e.g., if there is low inter-correlation among attributes/cues). Since our work assumes a “hit or miss” decision criterion, we conclude by outlining extensions for exploring the effects of different loss functions.Decision making, Bounded rationality, Lexicographic rules, Choice theory, Leex

Choosing Suitable Indicators for the Assessment of Urban Air Mobility: A Case Study of Upper Bavaria, Germany

Technological advances are disrupting mobility patterns and transport technologies, both on the ground and in the air. The latter has been recently observed in the research community of urban air mobility (UAM). Research in this area has studied several areas of its implementation, such as vehicle concepts, infrastructure, transport modeling, or operational constraints. Few studies however have focused on evaluating this service as an alternative among existing transportation systems. This research presents an approach for the selection of indicators for a multi-criteria analysis for the assessment of UAM, in a case study of Upper Bavaria, Germany. A 5-stage approach is showcased including an expert assessment for the relevance and feasibility of indicators, based on two rating scales. A threshold for selection is presented, applied and validated for both scales. The results included a list of indicators for assessing the potentials of UAM integration to existing public transportation systems; the chosen indicators were then compared against existing ones for sustainable urban mobility. A high match between resulting indicators and previous ones further validate the results, and suggest that there is a need for an iterative approach in the assessment of disruptive transport technologies

Decision aid system founded on nonlinear valuation, dispersion-based weighting and correlative aggregation for wire rope selection in slope stability cable nets

This paper presents a decision aid system to address hierarchically structured decision-making problems based on the determination of the satisfaction provided by a group of alternatives in relation to multiple conflicting subcriteria grouped into criteria. The system combines the action of three new methods related to the following concepts: nonlinear valuation, dispersion-based weighting and correlative aggregation. The first includes five value functions that allow the conversion of the ratings of the alternatives regarding the subcriteria into the satisfaction they produce in a versatile and simple manner through the Beta Cumulative Distribution Function. The use of measures of dispersion to weight the subcriteria by giving more importance to those factors that can make a difference due to their heterogeneity is revised to validate it when the values are not normally distributed. Dependencies between subcriteria are taken into account through the determination of their correlation coefficients, whose incorporation adjusts the results provided by the system to favour those alternatives having a balanced behaviour with respect to conflicting aspects. The overall satisfaction provided by each alternative is determined using a prioritisation operator to avoid compensation between criteria when aggregating the subcriteria. The system was tested through a novel field of application such as the selection of wire rope to form slope stability cable nets.The authors wish to express their gratitude to the IP department of INCHALAM S.A., whose collaboration and support made this paper possible

Semantic Similarity of Spatial Scenes

The formalization of similarity in spatial information systems can unleash their functionality and contribute technology not only useful, but also desirable by broad groups of users. As a paradigm for information retrieval, similarity supersedes tedious querying techniques and unveils novel ways for user-system interaction by naturally supporting modalities such as speech and sketching. As a tool within the scope of a broader objective, it can facilitate such diverse tasks as data integration, landmark determination, and prediction making. This potential motivated the development of several similarity models within the geospatial and computer science communities. Despite the merit of these studies, their cognitive plausibility can be limited due to neglect of well-established psychological principles about properties and behaviors of similarity. Moreover, such approaches are typically guided by experience, intuition, and observation, thereby often relying on more narrow perspectives or restrictive assumptions that produce inflexible and incompatible measures. This thesis consolidates such fragmentary efforts and integrates them along with novel formalisms into a scalable, comprehensive, and cognitively-sensitive framework for similarity queries in spatial information systems. Three conceptually different similarity queries at the levels of attributes, objects, and scenes are distinguished. An analysis of the relationship between similarity and change provides a unifying basis for the approach and a theoretical foundation for measures satisfying important similarity properties such as asymmetry and context dependence. The classification of attributes into categories with common structural and cognitive characteristics drives the implementation of a small core of generic functions, able to perform any type of attribute value assessment. Appropriate techniques combine such atomic assessments to compute similarities at the object level and to handle more complex inquiries with multiple constraints. These techniques, along with a solid graph-theoretical methodology adapted to the particularities of the geospatial domain, provide the foundation for reasoning about scene similarity queries. Provisions are made so that all methods comply with major psychological findings about people’s perceptions of similarity. An experimental evaluation supplies the main result of this thesis, which separates psychological findings with a major impact on the results from those that can be safely incorporated into the framework through computationally simpler alternatives

Simplified models for multi-criteria decision analysis under uncertainty

Includes abstract.Includes bibliographical references.When facilitating decisions in which some performance evaluations are uncertain, a decision must be taken about how this uncertainty is to be modelled. This involves, in part, choosing an uncertainty format {a way of representing the possible outcomes that may occur. It seems reasonable to suggest {and is an aim of the thesis to show {that the choice of how uncertain quantities are represented will exert some influence over the decision-making process and the final decision taken. Many models exist for multi-criteria decision analysis (MCDA) under conditions of uncertainty; perhaps the most well-known are those based on multi-attribute utility theory [MAUT, e.g. 147], which uses probability distributions to represent uncertainty. The great strength of MAUT is its axiomatic foundation, but even in its simplest form its practical implementation is formidable, and although there are several practical applications of MAUT reported in the literature [e.g. 39, 270] the number is small relative to its theoretical standing. Practical applications often use simpler decision models to aid decision making under uncertainty, based on uncertainty formats that `simplify' the full probability distributions (e.g. using expected values, variances, quantiles, etc). The aim of this thesis is to identify decision models associated with these `simplified' uncertainty formats and to evaluate the potential usefulness of these models as decision aids for problems involving uncertainty. It is hoped that doing so provides some guidance to practitioners about the types of models that may be used for uncertain decision making. The performance of simplified models is evaluated using three distinct methodological approaches {computer simulation, `laboratory' choice experiments, and real-world applications of decision analysis {in the hope of providing an integrated assessment. Chapter 3 generates a number of hypothetical decision problems by simulation, and within each problem simulates the hypothetical application of MAUT and various simplified decision models. The findings allow one to assess how the simplification of MAUT models might impact results, but do not provide any general conclusions because they are based on hypothetical decision problems and cannot evaluate practical issues like ease-of-use or the ability to generate insight that are critical to good decision aid. Chapter 4 addresses some of these limitations by reporting an experimental study consisting of choice tasks presented to numerate but unfacilitated participants. Tasks involved subjects selecting one from a set of five alternatives with uncertain attribute evaluations, with the format used to represent uncertainty and the number of objectives for the choice varied as part of the experimental design. The study is limited by the focus on descriptive rather than real prescriptive decision making, but has implications for prescriptive decision making practice in that natural tendencies are identified which may need to be overcome in the course of a prescriptive analysis

On heuristic and linear models of judgment: Mapping the demand for knowledge

Research on judgment and decision making presents a confusing picture of human abilities. For example, much research has emphasized the dysfunctional aspects of judgmental heuristics, and yet, other findings suggest that these can be highly effective. A further line of research has modeled judgment as resulting from “as if” linear models. This paper illuminates the distinctions in these approaches by providing a common analytical framework based on the central theoretical premise that understanding human performance requires specifying how characteristics of the decision rules people use interact with the demands of the tasks they face. Our work synthesizes the analytical tools of “lens model” research with novel methodology developed to specify the effectiveness of heuristics in different environments and allows direct comparisons between the different approaches. We illustrate with both theoretical analyses and simulations. We further link our results to the empirical literature by a meta-analysis of lens model studies and estimate both human and heuristic performance in the same tasks. Our results highlight the trade-off between linear models and heuristics. Whereas the former are cognitively demanding, the latter are simple to use. However, they require knowledge – and thus “maps” – of when and which heuristic to employ.Decision making; heuristics; linear models; lens model; judgmental biases

STUDY TOWARDS THE TIME-BASED MCDA RANKING ANALYSIS – A SUPPLIER SELECTION CASE STUDY

Decision-making processes increasingly use models based on various methods to ensure professional analysis and evaluation of the considered alternatives. However, the abundance of these methods makes it difficult to choose the proper method to solve a given problem. Also, it is worth noting whether different results can be obtained using different methods within a single decision problem. In this paper, we used three selected Multi-Criteria Decision Analysis (MCDA) methods called COMET, TOPSIS, and SPOTIS in order to examine how the obtained rankings vary. The selection of material suppliers was taken into consideration. The equal weights, entropy and standard deviation methods were used to determine the weights for criteria. Final preferences values were then compared with the WS similarity coefficient and weighted Spearman correlation coefficient to check the similarity of the received rankings. It was noticed that in the given problem, all of the methods provide highly correlated results, and the obtained positional rankings are not significantly different. However, practical conclusions indicate the need to look for improved solutions in the correct and accurate assessment of suppliers in a given period

Multiple Attribute Decision Making Based on Cross-Evaluation with Uncertain Decision Parameters

Multiple attribute decision making (MADM) problem is one of the most common and popular research fields in the theory of decision science. A variety of methods have been proposed to deal with such problems. Nevertheless, many of them assumed that attribute weights are determined by different types of additional preference information which will result in subjective decision making. In order to solve such problems, in this paper, we propose a novel MADM approach based on cross-evaluation with uncertain parameters. Specifically, the proposed approach assumes that all attribute weights are uncertain. It can overcome the drawback in prior research that the alternatives’ ranking may be determined by a single attribute with an overestimated weight. In addition, the proposed method can also balance the mean and deviation of each alternative’s cross-evaluation score to guarantee the stability of evaluation. Then, this method is extended to a more generalized situation where the attribute values are also uncertain. Finally, we illustrate the applicability of the proposed method by revisiting two reported studies and by a case study on the selection of community service companies in the city of Hefei in China

Analyzing the Determinants of the Matching Public School Teachers to Jobs: Estimating Compensating Differentials in Imperfect Labor Markets

Although there is growing recognition of the contribution of teachers to students' educational outcomes, there are large gaps in our understanding of how teacher labor markets function. Most research on teacher labor markets use models developed for the private sector. However, markets for public school teachers differ in fundamental ways from those in the private sector. Collective bargaining and public decision making processes set teacher salaries. Thus it is unlikely that wages adjust quickly to equilibrate the supply and demand for worker and job attributes. The objective of this paper is to develop and estimate a model that more accurately characterizes the institutional features of teacher labor markets. The approach is based on a game-theoretic two-sided matching model and the estimation strategy employs the method of simulated moments. With this combination, we are able to estimate how factors affect the choices of individual teachers and hiring authorities, as well as how these choices interact to determine the equilibrium allocation of teachers across jobs. Even though this paper focuses on worker-job match within teacher labor markets, many of the issues raised and the empirical framework employed are relevant in other settings where wages are set administratively or, more generally, do not clear the pertinent markets for job and worker attributes.